Two papers were published in MLCAD, the new ACM/IEEE conference focusing on the machine learning techniques for design automation techniques. 

The first paper by Ph.D. student Shaoyi Peng developed an auto-encoder/decoder like DNN networks for analysis TDDB reliability effects. 

The second paper by Ph.D. student Jinwei Zhang developed an efficient reinforcement learning based hotspot-aware task mapping method for dynamic reliability management of multi-core processors. 

• S. Peng, W. Jin, L. Chen, and S. X.-D. Tan, "Data-driven fast electrostatics and TDDB aging analysis", Proc. of the 2020 ACM/IEEE Workshop on Machine Learning for CAD (MLCAD'20), Virtual Event, Nov. 2020
• J. Zhang, S. Sadiqbatcha, Y. Gao, M. O’Dea, N. Yu, and S. X.-D. Tan, “HAT-DRL: Hotspot-Aware Task Mapping for Lifetime Improvement of Multicore System using Deep Reinforcement Learning”, Proc. 2nd IEEE/ACM Workshop on Machine Learning for CAD (MLCAD’20), Virtual Event, Nov. 2020.